Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
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| URI | http://purl.tuc.gr/dl/dias/2D869BA2-66D3-4936-9319-9680FCEE2DD3 | - |
| Αναγνωριστικό | http://www.tuc.gr/fileadmin/users_data/elci/Kalaitzakis/J.25.pdf | - |
| Αναγνωριστικό | https://doi.org/10.1016/S0262-8856(03)00004-0 | - |
| Γλώσσα | en | - |
| Μέγεθος | 23 | en |
| Τίτλος | A survey of video processing techniques for traffic applications. | en |
| Δημιουργός | Kastrinaki V. | en |
| Δημιουργός | Zervakis Michalis | en |
| Δημιουργός | Ζερβακης Μιχαλης | el |
| Δημιουργός | Kalaitzakis Kostas | en |
| Δημιουργός | Καλαϊτζακης Κωστας | el |
| Εκδότης | Elsevier | en |
| Περιγραφή | Δημοσίευση σε επιστημονικό περιοδικό | el |
| Περίληψη | Video sensors become particularly important in traffic applications mainly due to their fast response, easy installation, operation and maintenance, and their ability to monitor wide areas. Research in several fields of traffic applications has resulted in a wealth of video processing and analysis methods. Two of the most demanding and widely studied applications relate to traffic monitoring and automatic vehicle guidance. In general, systems developed for these areas must integrate, amongst their other tasks, the analysis of their static environment (automatic lane finding) and the detection of static or moving obstacles (object detection) within their space of interest. In this paper we present an overview of image processing and analysis tools used in these applications and we relate these tools with complete systems developed for specific traffic applications. More specifically, we categorize processing methods based on the intrinsic organization of their input data (feature-driven, area-driven, or model-based) and the domain of processing (spatial/frame or temporal/video). Furthermore, we discriminate between the cases of static and mobile camera. Based on this categorization of processing tools, we present representative systems that have been deployed for operation. Thus, the purpose of the paper is threefold. First, to classify image-processing methods used in traffic applications. Second, to provide the advantages and disadvantages of these algorithms. Third, from this integrated consideration, to attempt an evaluation of shortcomings and general needs in this field of active research. | en |
| Τύπος | Peer-Reviewed Journal Publication | en |
| Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | el |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2015-09-30 | - |
| Ημερομηνία Δημοσίευσης | 2003 | - |
| Θεματική Κατηγορία | Detection, Traffic | en |
| Θεματική Κατηγορία | Monitoring, Traffic | en |
| Θεματική Κατηγορία | Surveillance, Traffic | en |
| Θεματική Κατηγορία | Traffic detection | en |
| Θεματική Κατηγορία | Traffic surveillance | en |
| Θεματική Κατηγορία | traffic monitoring | en |
| Θεματική Κατηγορία | detection traffic | en |
| Θεματική Κατηγορία | monitoring traffic | en |
| Θεματική Κατηγορία | surveillance traffic | en |
| Θεματική Κατηγορία | traffic detection | en |
| Θεματική Κατηγορία | traffic surveillance | en |
| Θεματική Κατηγορία | Automatic vehicle guidance | en |
| Θεματική Κατηγορία | Automatic lane finding | en |
| Θεματική Κατηγορία | Object detection | en |
| Θεματική Κατηγορία | Dynamic scene analysis | en |
| Βιβλιογραφική Αναφορά | V. Kastrinaki, M. Zervakis and K. Kalaitzakis, "A survey of video processing techniques for traffic applications," Image and Vision Computing, vol. 21, no. 4, pp. 359-381, Apr. 2003. doi:10.1016/S0262-8856(03)00004-0 | en |
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